Nurgle

"Buboes, phlegm, blood and guts! Boils, bogeys, rot and pus! Blisters, fevers, weeping sores! From your wounds the fester pours."

-- The Chant of Nurgle

The following analyses require jupyterthemes, numpy, pandas, and plotly. Install them with pip.

In [1]:
from jupyterthemes.stylefx import set_nb_theme
set_nb_theme('grade3')
Out[1]:
In [2]:
import os
PREFIX = os.environ.get('PWD', '.')
# PREFIX = "../build/outputs"
In [3]:
import numpy
import pandas
In [4]:
import plotly.graph_objs as go
import plotly.figure_factory as ff
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)

Inputs

Reading input files for the simulation.

In [5]:
compounds = pandas.read_csv(os.path.join(PREFIX, "compounds.csv"))
num_compounds = compounds.shape[0]
print('[{}] compounds were loaded.'.format(num_compounds))
metabolism = pandas.read_csv(os.path.join(PREFIX, "metabolism.csv"))
print('[{}] reactions were loaded.'.format(metabolism.shape[0]))

[525] compounds were loaded.
[512] reactions were loaded.

Metabolism

Here shows profiles and analyses related to the metabolic pathway and chemical compounds.

Concentration of Compounds

Reading timecourse data first.

In [6]:
timecourse = pandas.read_csv(os.path.join(PREFIX, "timecourse.csv"))
timecourse = timecourse.rename(columns={timecourse.columns[0]: "Time"})
pddata = pandas.DataFrame(timecourse.values[: , : num_compounds], timecourse.index, timecourse.columns[ : num_compounds])
indices = [0] + list(range(num_compounds, timecourse.shape[1]))
fluxes = pandas.DataFrame(timecourse.values[: , indices], timecourse.index, timecourse.columns[indices])

Plotting concentrations of compounds.

In [8]:
plot1(pddata, "compound_markers", nsteps=15)

Plotting time series of compound concentrations.

In [10]:
plot2(pddata, "compound_lines")
In [12]:
plot3(fluxes, "flux_markers", nsteps=15)